Senior/Principal Visual ML Engineer

Clarvos LLC•Charleston, SC

19h•Remote

About The Position

We are seeking a Senior/Principal Machine Learning Engineer with deep expertise in Visual Language Models (VLMs), Large Vision Models (LVMs), Generative AI, and multimodal foundation models to build the next generation of AI-powered creative technologies. This is a hands-on technical role responsible for architecting, developing, and deploying state-of-the-art AI systems for image, video, and creative generation. You will work closely with Product, Engineering, Data Science, and Design teams to build production-scale GenAI capabilities that power creative automation, digital advertising, content personalization, and campaign optimization. You will drive innovation across the entire lifecycle, from research and experimentation through large-scale production deployment, while helping establish the company's long-term Visual AI strategy.

Requirements

MS or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, Robotics, or a related field.
8–12+ years of experience developing production ML systems.
5+ years of experience in Deep Learning and Computer Vision.
3+ years of hands-on experience with Generative AI for images and video.
Expert-level proficiency in Python and PyTorch.
Strong software engineering fundamentals with production-quality code.
Solid understanding of distributed systems, GPU optimization, batching, and cost-aware inference.
Excellent software engineering fundamentals (Python, APIs, microservices, Docker, Kubernetes).
Deep expertise in Generative AI, multimodal foundation models, Vision Language Models (VLMs), Large Vision Models (LVMs), diffusion models, transformers, and autoregressive architectures, with hands-on experience building image and video generation systems using leading models such as FLUX, Stable Diffusion, Imagen, Veo, Runway, Kling, and open-source video diffusion models.
Strong experience with computer vision and multimodal AI frameworks (CLIP, Florence, Qwen-VL, LLaVA, SAM, YOLO, Grounding DINO) and applying them to visual understanding, generation, editing, and creative optimization.

Nice To Haves

Proven ability to productionize large-scale AI models using modern ML infrastructure including Hugging Face, Diffusers, DeepSpeed, FSDP, TensorRT, ONNX, CUDA/GPU optimization, and cloud-native MLOps platforms (AWS/GCP/Azure, Kubernetes, Kubeflow, MLflow, distributed inference).

Responsibilities

Design and develop production-grade AI systems for image generation, video generation, image editing and enhancement, creative optimization, style transfer, multimodal content understanding, brand-aware content generation, and AI-assisted creative workflows.
Build scalable pipelines for automated creative generation across multiple marketing channels.
Research and implement state-of-the-art diffusion, transformer, autoregressive, and multimodal architectures.
Fine-tune and optimize foundation models for enterprise production use cases.
Develop and optimize systems using Vision Language Models (VLMs), Large Vision Models (LVMs), Multimodal LLMs, Diffusion models, Transformer-based image/video generation, Contrastive vision-language models, and Image-text alignment models.
Train, fine-tune, optimize, and deploy large-scale generative AI models using advanced techniques including LoRA, QLoRA, PEFT, distillation, quantization, and prompt optimization.
Build robust model evaluation frameworks to measure creative quality, visual fidelity, consistency, brand alignment, safety, hallucination risk, and human preference alignment.
Improve model performance across quality, latency, scalability, and cost through continuous experimentation, benchmarking, and production optimization.
Lead development and building of AI systems for text-to-video generation, image-to-video generation, video editing, AI avatars, motion transfer, storyboarding, creative sequencing, marketing video generation, and dynamic creative optimization.
Lead decisions around foundation models, fine-tuning strategies, RAG pipelines, embeddings, and ranking systems.
Architect and oversee scalable LLM/GenAI systems for MarTech/AdTech use cases.
Design and deploy multi-agent systems using frameworks such as LangGraph, AutoGen, CrewAI, MCP, or equivalent.
Own end-to-end ML system design: data ingestion, feature pipelines, training, inference, evaluation, and monitoring.
Work closely with Product, Data, and Platform teams to translate business needs into scalable ML capabilities.
Communicate complex ML concepts clearly to executive leadership, stakeholders, and the Board.
Contribute to technical narratives used for fundraising, company valuation, and strategic planning.